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Rydberg atom arrays with Van der Waals interactions provide a controllable path to simulate the locally connected transverse-field Ising model (TFIM), a prototypical model in statistical mechanics. Remotely operating the publicly accessible Aquila Rydberg atom array, we experimentally investigate the physics of TFIM far from equilibrium and uncover significant deviations from the theoretical predictions. Rather than the expected ballistic spread of correlations, the Rydberg simulator exhibits a sub-ballistic spread, along with a logarithmic scaling of entanglement entropy in time - all while the system mostly retains its initial magnetization. By modeling the atom motion in tweezer traps, we trace these effects to an emergent natural disorder in Rydberg atom arrays, which we characterize with a minimal random spin model. We further experimentally explore the different dynamical regimes hosted in the system by varying the lattice spacing and the Rabi frequency. Our findings highlight the crucial role of atom motion in the many-body dynamics of Rydberg atom arrays at the TFIM limit, and propose simple benchmark measurements to test for its presence in future experiments. 

Abstract BackgroundDelta radiomics is a high‐throughput computational technique used to describe quantitative changes in serial, time‐series imaging by considering the relative change in radiomic features of images extracted at two distinct time points. Recent work has demonstrated a lack of prognostic signal of radiomic features extracted using this technique. We hypothesize that this lack of signal is due to the fundamental assumptions made when extracting features via delta radiomics, and that other methods should be investigated. PurposeThe purpose of this work was to show a proof‐of‐concept of a new radiomics paradigm for sparse, time‐series imaging data, where features are extracted from a spatial‐temporal manifold modeling the time evolution between images, and to assess the prognostic value on patients with oropharyngeal cancer (OPC). MethodsTo accomplish this, we developed an algorithm to mathematically describe the relationship between two images acquired at time and . These images serve as boundary conditions of a partial differential equation describing the transition from one image to the other. To solve this equation, we propagate the position and momentum of each voxel according to Fokker–Planck dynamics (i.e., a technique common in statistical mechanics). This transformation is driven by an underlying potential force uniquely determined by the equilibrium image. The solution generates a spatial‐temporal manifold (3 spatial dimensions + time) from which we define dynamic radiomic features. First, our approach was numerically verified by stochastically sampling dynamic Gaussian processes of monotonically decreasing noise. The transformation from high to low noise was compared between our Fokker–Planck estimation and simulated ground‐truth. To demonstrate feasibility and clinical impact, we applied our approach to¹⁸F‐FDG‐PET images to estimate early metabolic response of patients (n = 57) undergoing definitive (chemo)radiation for OPC. Images were acquired pre‐treatment and 2‐weeks intra‐treatment (after 20 Gy). Dynamic radiomic features capturing changes in texture and morphology were then extracted. Patients were partitioned into two groups based on similar dynamic radiomic feature expression via k‐means clustering and compared by Kaplan–Meier analyses with log‐rank tests (p < 0.05). These results were compared to conventional delta radiomics to test the added value of our approach. ResultsNumerical results confirmed our technique can recover image noise characteristics given sparse input data as boundary conditions. Our technique was able to model tumor shrinkage and metabolic response. While no delta radiomics features proved prognostic, Kaplan–Meier analyses identified nine significant dynamic radiomic features. The most significant feature was Gray‐Level‐Size‐Zone‐Matrix gray‐level variance (p = 0.011), which demonstrated prognostic improvement over its corresponding delta radiomic feature (p = 0.722). ConclusionsWe developed, verified, and demonstrated the prognostic value of a novel, physics‐based radiomics approach over conventional delta radiomics via data assimilation of quantitative imaging and differential equations. 

Abstract The electrocatalytic hydrogen evolution reaction (HER) is one of the most studied and promising processes for hydrogen fuel generation. Single-atom catalysts have been shown to exhibit ultra-high HER catalytic activity, but the harsh preparation conditions and the low single-atom loading hinder their practical applications. Furthermore, promoting hydrogen evolution reaction kinetics, especially in alkaline electrolytes, remains as an important challenge. Herein, Pt/C₆₀catalysts with high-loading, high-dispersion single-atomic platinum anchored on C₆₀are achieved through a room-temperature synthetic strategy. Pt/C₆₀-2 exhibits high HER catalytic performance with a low overpotential (η₁₀) of 25 mV at 10 mA cm⁻². Density functional theory calculations reveal that the Pt-C₆₀polymeric structures in Pt/C₆₀-2 favors water adsorption, and the shell-like charge redistribution around the Pt-bonding region induced by the curved surfaces of two adjacent C₆₀facilitates the desorption of hydrogen, thus favoring fast reaction kinetics for hydrogen evolution. 

Centromeres are long, often repetitive regions of genomes that bind kinetochore proteins and ensure normal chromosome segregation. Engineering centromeres that function in vivo has proven to be difficult. Here we describe a tethering approach that activates functional maize centromeres at synthetic sequence arrays. A LexA-CENH3 fusion protein was used to recruit native Centromeric Histone H3 (CENH3) to long arrays of LexO repeats on a chromosome arm. Newly recruited CENH3 was sufficient to organize functional kinetochores that caused chromosome breakage, releasing chromosome fragments that were passed through meiosis and into progeny. Several fragments formed independent neochromosomes with centromeres localized over the LexO repeat arrays. The new centromeres were self-sustaining and transmitted neochromosomes to subsequent generations in the absence of the LexA-CENH3 activator. Our results demonstrate the feasibility of using synthetic centromeres for karyotype engineering applications. 

Objectives: The COVID-19 pandemic impacted food systems, health systems and the environment globally, with potentially greater negative effects in many lower-middle income countries (LMICs) including Indonesia. The purpose of this qualitative study was to investigate the potential impacts of the COVID-19 pandemic on diets, health and the marine environment in Indonesia, based on the perspectives of a multidisciplinary group of informants. Methods: We conducted remote in-depth interviews with 27 key informants from many regions of Indonesia, who are either healthcare providers, nutrition researchers or environmental researchers. Interview question guides were developed based on a socio-ecological framework. We analyzed the data using a qualitative content analysis approach. Results: Informants suggested that while the COVID-19 brought increased awareness about and adherence to good nutrition and health behaviors, the impact was transitory. Informants indicated that healthy food options became less affordable, due to job losses and reduced income, suggesting a likely increase in food insecurity and obesity. Environmental researchers described higher levels of marine pollution from increase in hygienic wastes as well as from plastic packaging from food orders. Conclusions: Our findings reveal perceptions by informants that the increased awareness and adherence to health behaviors observed during the pandemic was not sustained. Our results also suggest that the pandemic may have exacerbated the double-burden paradox and marine pollution in Indonesia. This study offers information for generating hypotheses for quantitative studies to corroborate our findings and inform policies and programs to mitigate the long-term impacts of the COVID-19 on diets, health, and the marine environment in Indonesia. 

Purpose To develop a method of biologically guided deep learning for post-radiation 18 FDG-PET image outcome prediction based on pre-radiation images and radiotherapy dose information. Methods Based on the classic reaction–diffusion mechanism, a novel biological model was proposed using a partial differential equation that incorporates spatial radiation dose distribution as a patient-specific treatment information variable. A 7-layer encoder–decoder-based convolutional neural network (CNN) was designed and trained to learn the proposed biological model. As such, the model could generate post-radiation 18 FDG-PET image outcome predictions with breakdown biological components for enhanced explainability. The proposed method was developed using 64 oropharyngeal patients with paired 18 FDG-PET studies before and after 20-Gy delivery (2 Gy/day fraction) by intensity-modulated radiotherapy (IMRT). In a two-branch deep learning execution, the proposed CNN learns specific terms in the biological model from paired 18 FDG-PET images and spatial dose distribution in one branch, and the biological model generates post-20-Gy 18 FDG-PET image prediction in the other branch. As in 2D execution, 718/233/230 axial slices from 38/13/13 patients were used for training/validation/independent test. The prediction image results in test cases were compared with the ground-truth results quantitatively. Results The proposed method successfully generated post-20-Gy 18 FDG-PET image outcome prediction with breakdown illustrations of biological model components. Standardized uptake value (SUV) mean values in 18 FDG high-uptake regions of predicted images (2.45 ± 0.25) were similar to ground-truth results (2.51 ± 0.33). In 2D-based Gamma analysis, the median/mean Gamma Index (<1) passing rate of test images was 96.5%/92.8% using the 5%/5 mm criterion; such result was improved to 99.9%/99.6% when 10%/10 mm was adopted. Conclusion The developed biologically guided deep learning method achieved post-20-Gy 18 FDG-PET image outcome predictions in good agreement with ground-truth results. With the breakdown biological modeling components, the outcome image predictions could be used in adaptive radiotherapy decision-making to optimize personalized plans for the best outcome in the future. 

Abstract Fully internal and motional state controlled and individually manipulable polar molecules are desirable for many quantum science applications leveraging the rich state space and intrinsic interactions of molecules. While prior efforts at assembling molecules from their constituent atoms individually trapped in optical tweezers achieved such a goal for exactly one molecule (Zhang J T et al 2020 Phys. Rev. Lett. 124 253401; Cairncross W B et al 2021 Phys. Rev. Lett. 126 123402; He X et al 2020 Science 370 331–5), here we extend the technique to an array of five molecules, unlocking the ability to study molecular interactions. We detail the technical challenges and solutions inherent in scaling this system up. With parallel preparation and control of multiple molecules in hand, this platform now serves as a starting point to harness the vast resources and long-range dipolar interactions of molecules.